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Artificial Analysts Is More Accurate To Compare Economic Analysts

Artificial Analysts Is More Accurate To Compare Economic Analysts

          
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About the Book

It seems that in future, (AI) machine learning will allow search to evolve even further. Search engineers will deliver refined recommendations to their business users and use less human input to predict consumers' needs. For IBM computer example, it indicated 90% of the data that exists today has been created in the last two years. This huge explosion of data gives brands the opportunity to quickly spot and react to the latest trends, fashion and fads among its clients and potential clients. This will allow companies to better engage with younger consumers, who gain influence access to the latest trends, and use the brands. They associate with to help define who they are as individuals. Thus, brands have to identify and make use of them before consumers move on, but the vast quantity of data available makes. This a resource-intensive task. For next example, Lesara, a based online clothes store, uses this machine learning to inform its product decision often gathering information from internal and external sources. When its trends -spotting shoes. Lesara has a range of over 20 styles and sells hundreds of pairs a day. It focus on giving consumers, the very latest trends allow Lesara to develop on average of 50,000 new items each year. It compared to 11,000 old items each year. Thus, (AI) brain seems to human brain to own analytical ability to predict consumer behaviors. For another example, Lesara is one online clothes store, uses machine learning decisions after gathering information from internal and external sources. One of its most popular products, shoes with LED started life when its trend spotting software flagged up a blogger wearing similar shoes. Now Lesara has a range of over 20 styles and sells hundreds of pairs a day. Its focus on giving consumers the very latest trends allows Lesara to develop an average of 50,000 new items each year, compared to 11,000 for its competitor Lara. it seems (AI) machine learning can help Lesara business to predict what kinds of shoes design or style that shoe consumers will prefer choose to buy in future shoe market trend. Thus, Lesara can predict shoe consumers' taste successfully and it can manufacture many attractive style of shoes. (AI) machine learning can gather global past shoe consumer's shoe shopping experiences, then analyzes to make conclusion to give lesara recommendation successfully. This will make the experience more enjoyable for shoe consumers and allow Lesara to advert whose different new style or design of shoes to deliver them move relevant messages by understanding the context of the experience.However, (AI) machine learning will have this risk who manufacturers need to concern if they applied this technology to predict consumer behavior. It is on sample consumers' privacy issue, in order to avoid complaint chance occurrence. However, machine learning can tie this data together to identify which f the billions of devices are being used by individual consumers. This helps brands understand how consumer engagement and actions can be attributed to different messages in different contexts and at different time. So, machine learning can help brands to build confidence to promote their products by any advertisement channels. When, this new (AI) machine learning technology can conclude how to design their products to be the most attractive, due to it has more accurate to predict consumer behaviors to compare human themselves prediction judgement effort. It seems that (AI) machine judgement effort is more accurate to compare to human judgment effort.


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Product Details
  • ISBN-13: 9798697315484
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 254 mm
  • No of Pages: 46
  • Series Title: Robotic Education Market
  • Weight: 145 gr
  • ISBN-10: 8697315484
  • Publisher Date: 13 Oct 2020
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Spine Width: 3 mm
  • Width: 203 mm


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